Exploratory Analysis of Demographic Characteristics Where Facilities are Located

Analysis at the Block Group Level

# can use this function to see what variables are available for the given dataset
# listCensusMetadata(name = "pdb/blockgroup", vintage = 2021, type = "variables", group = NULL)


# get census data for the variables listed at the blockgroup level; block level not available 
get_census_data_safe <- function(state_code) {
  region_for_state = paste0("state:", state_code, "+county:*+tract:*")
  result <- tryCatch( 
    {
    getCensus(name = "pdb/blockgroup",
                      vintage = 2021,
                      key = "fbbc8b0d3e53089da7cabc380628d6d46ae00444",
                      vars = c("Block_group",
                               "County", 
                               "URBANIZED_AREA_POP_CEN_2010",
                               "Tot_Population_CEN_2010", 
                               "Tot_Population_ACS_15_19", 
                               "Males_ACS_15_19", 
                               "Females_ACS_15_19",
                               "Median_Age_ACS_15_19", 
                               "Hispanic_ACS_15_19",
                               "NH_White_alone_ACS_15_19",
                               "NH_Blk_alone_ACS_15_19",
                               "NH_AIAN_alone_ACS_15_19",
                               "NH_Asian_alone_ACS_15_19",
                               "NH_NHOPI_alone_ACS_15_19", 
                               "NH_SOR_alone_ACS_15_19",
                               "Pov_Univ_ACS_15_19",
                               "Prs_Blw_Pov_Lev_ACS_15_19",
                               "No_Health_Ins_ACS_15_19",
                               "Tot_Occp_Units_ACS_15_19",
                               "Aggregate_HH_INC_ACS_15_19",
                               "Med_HHD_Inc_BG_ACS_15_19",
                               "Tot_Prns_in_HHD_ACS_15_19",
                               "Renter_Occp_HU_ACS_15_19",
                               "Med_House_Value_BG_ACS_15_19",
                               "pct_ENG_VW_ACS_15_19",
                               "pct_URBANIZED_AREA_POP_CEN_2010",
                               "pct_RURAL_POP_CEN_2010",
                               "pct_Inst_GQ_CEN_2010",
                               "pct_Non_Inst_GQ_CEN_2010",
                               "pct_URBAN_CLUSTER_POP_CEN_2010",
                               "pct_PUB_ASST_INC_ACS_15_19"
                               ),
                      regionin = region_for_state,
                      region = "block group:*")
          } ,
      error=function(e) {
            return()
        } 
      )
  return(result)
}


# get block group level data for every state in state_codes - only run when making changes to the variables included in the function above
# blockgroup_level_data <- map_dfr(state_codes,  ~get_census_data_safe(.x))

# remove of redundant columns
# blockgroup_level_data <- blockgroup_level_data %>% 
#  select(-Block_group, County)

# write_csv(blockgroup_level_data, "./blockgroup_level_data.csv")

blockgroup_level_data <- read_csv("./blockgroup_level_data.csv")

# get block group number, which is the first digit of the block number 
prison_boundaries_blocks <- prison_boundaries_blocks %>% 
  mutate(block_group = as.numeric(substr(BLOCKCE10, 1,1)))

# join prison boundaries data to the census data 
prison_boundaries_block_census <- left_join(prison_boundaries_blocks, blockgroup_level_data,
                                            by = c("STATEFP10" = "state",
                                                   "COUNTYFP10" = "county",
                                                   "TRACTCE10" = "tract",
                                                   "block_group" = "block_group"))


# all variables in the dataset 
all_vars <- prison_boundaries_block_census %>% colnames()

Variable definitions are from the documentation for the 2021 Planning Database.

First, we can look at the distributions of the median age in block groups where facilities are located.

Distribution of Median Age

Distribution of the Percentage Urbanized

As defined in the documentation, the variable definitions are as follows:
* pct_URBANIZED_AREA_POP_CEN_2010: “percentage of the 2010 Census total population that lives in a densely settled area containing 50,000 or more people”
* pct_URBAN_CLUSTER_POP_CEN_2010: “percentage of the 2010 Census total population that lives in a densely settled area containing 2,500 to 49,999 people”
* pct_RURAL_POP_CEN_2010: “percentage of the 2010 Census total population that lives outside of an urbanized area or urban cluster”

Percentage of Facilities in a Block Group that was Mostly Rural or Mostly Urban
Rural block groups are defined here as those where greater than 50% of the 2010 Census total population for that block group
was recorded to live outside of an urbanized area or urban cluster
Data from the 2010 Census
Density Percentage
Rural 31.12%
Urban 68.25%
Missing 0.64%
For Block Groups in the United States: Mean and Median Percentages in Urbanized, Urban Cluster, and Rural Areas
Data from the 2010 Census
Population Density Category Mean Percentage Median Percentage
Urban 69.00312 100
Urban Cluster 10.30077 0
Rural 20.69611 0
For Block Groups Where Facilities are Located: Mean and Median Percentages in Urbanized, Urban Cluster, and Rural Areas
Data from the 2010 Census
Population Density Category Mean Percentage Median Percentage
Urban 36.63423 0.0
Urban Cluster 29.42237 0.0
Rural 33.94341 7.6

We can also see how the distribution of age varies depending on whether blocks are primarily urban or rural, among different facility types, and among different facility security levels.

Distribution of Racial Percentages for each Racial Category

# prison_blocks_race

# 
#  mutate(sum_all = Hispanic_ACS_15_19 + NH_AIAN_alone_ACS_15_19 + NH_Asian_alone_ACS_15_19 + NH_Blk_alone_ACS_15_19 + NH_NHOPI_alone_ACS_15_19 + NH_SOR_alone_ACS_15_19 + NH_White_alone_ACS_15_19) %>% View()

# for race reference csv, copied and pasted definitions at link here https://api.census.gov/data/2021/pdb/blockgroup/variables.html into excel file 
race_ref <- read_csv("./races_reference.csv", col_names = FALSE) %>% 
  select(X1, X2) %>% 
  rename(`Racial Category` = X1,
         Description = X2) %>%
  mutate(Description = str_replace(Description, "in the ACS", ""),
         Description = str_replace(Description, ",", ",\n")) %>%
  filter(!str_detect(Description, "Census") & !str_detect(Description, "MOE") )

blockgroup_level_data %>% 
  select(
         Hispanic_ACS_15_19, NH_AIAN_alone_ACS_15_19, 
         NH_Asian_alone_ACS_15_19, NH_Blk_alone_ACS_15_19, 
         NH_NHOPI_alone_ACS_15_19, NH_SOR_alone_ACS_15_19,
         NH_White_alone_ACS_15_19, Tot_Population_ACS_15_19) %>%
  mutate(across(1:ncol(.), ~ (.x / Tot_Population_ACS_15_19) * 100)) %>% # get percentage by dividing by total for that block
  select(-Tot_Population_ACS_15_19) %>%
  pivot_longer(1:ncol(.), names_to = "Racial Category", values_to  ="Percentage") %>%
  left_join(race_ref, by = c("Racial Category" = "Racial Category")) %>%
  group_by(Description) %>%
  mutate(Median = median(Percentage, na.rm = TRUE),
         Mean = mean(Percentage, na.rm = TRUE)) %>%
  ggplot(aes(x = Percentage)) + 
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE") +
  facet_wrap(~Description, scales = "free_y", ncol = 2) +
  scale_x_continuous(breaks = seq(0, 100, by = 10)) +
  theme_bw() + 
  labs(y = "Number of Block Groups", 
       x = "Percentage",
       subtitle = "Median in Red, Mean in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of Racial Percentages in Block Groups in the U.S.") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5))

Distribution of Percentage without Health Insurance

Distribution of Percentages Below the Poverty Level

Referencing the documentation:

Prs_Blw_Pov_Lev_ACS_15_19 is defined as the “Number of people classified as below the poverty level given their total family or household income within the last year, family size, and family composition in the ACS population”. To calculate the percentage, we can use the Pov_Univ_ACS_15_19 variable, which is defined as the “Population for whom poverty level is determined”.

Distribution of the Percentage of Housing Units Rented

Renter_Occp_HU_ACS_15_19 is defined as “Number of ACS occupied housing units that are not owner occupied, whether they are rented or occupied without payment of rent”.

Distribution of Average Household Size

Tot_Prns_in_HHD_ACS_15_19 is defined as the “All persons (household population) in ACS occupied housing unit. This is the numerator to calculate the average household size”. The denominator used to calculate the average household size is Tot_Occp_Units_ACS_15_19.

Distribution of Percentage Institutionalized

pct_Inst_GQ_CEN_2010 is defined as “The percentage of the 2010 Census population who live in group quarters and are primarily ineligible, unable, or unlikely to participate in labor force while residents. Institutional group quarters include correctional facilities for adults, juvenile facilities, nursing facilities, and other institutional facilities.”

Distribution of the Percentage Receiving Public Assistance Income

pct_PUB_ASST_INC_ACS_15_19 is the “percentage of all ACS occupied housing units that receive public assistance income (general assistance and Temporary Assistance to Needy Families).” There are two main categories of public assistance programs, social welfare programs and social insurance programs. Social welfare programs generally have low income criteria for eligibility, and include programs such as Supplemental Security Income (SSI), Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Temporary Assistance for Needy Families (TANF), and General Assistance (GA). Social insurance programs, meanwhile, are based on criteria outside income, such as age, employment status, or being a veteran. These include Social security (self and on behalf of a dependent child), Department of Veterans’ Affairs benefits (except Veteran’s pension), Unemployment insurance compensation, and Workers’ compensation.

Distribution of Percentage Where No Adult Speaks English “Very Well”

pct_ENG_VW_ACS_15_19 is defined as the “percentage of all ACS occupied housing units where no one ages 14 years and over speaks English only or speaks English ‘very well’”.

Tract Level Analysis

Some variables are not available at the block group level, but still may be relevant. These include the percentage of the population lacking a computing device, the percentage of the popoulation with broadband internet access and a computing device, and the percentage of the population 16 years and older who are unemployed.

Distribution of Percentages Lacking a Computing Device

The tract level data has additional variables available, and separate documentation outlining the definitions.

The variable pct_Pop_NoCompDevic_ACS_15_19 is defined as the “Percentage of people that live in households that do not have computing device of any kind in the ACS”.

Distribution of Percentages With Access to Broadband Wifi

The variable pct_Pop_w_BroadComp_ACS_15_19 is defined as the “Percentage of people that live in households that have both broadband Internet access and a computing device of any kind in the ACS”.

The thresholds for upload and download speeds are set by the Federal Communications Commission, and have changed over time, shaping our view on the digital divides that exist.

Distribution of the Percentage Unemployed

The variable pct_Civ_unemp_16p_ACS_15_19 is defined as the “percentage of ACS civilians ages 16 years and over in the labor force that are unemployed”.